Polar radius moment invariants criterion for identifying bearing early failure by chaotic oscillator

Yang LI; Zhi-bin Lin; Xiao-lin HUANG

doi:10.3788/OPE.20182602.0418

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Polar radius moment invariants criterion for identifying bearing early failure by chaotic oscillator

Micro/Nano Technology and Fine Mechanics | 更新时间：2020-07-05

- Polar radius moment invariants criterion for identifying bearing early failure by chaotic oscillator
- Optics and Precision Engineering Vol. 26, Issue 2, Pages: 418-425(2018)
- 作者机构：
  
  1.西南交通大学机械工程学院, 四川成都 610031
  2.中国地质科学院探矿工艺研究所, 四川成都 610081
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20182602.0418
  CLC： TH703
- Received：17 May 2017，
  
  Accepted：06 July 2017，
  
  Published：25 February 2018
- 稿件说明：
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Yang LI, Zhi-bin Lin, Xiao-lin HUANG. Polar radius moment invariants criterion for identifying bearing early failure by chaotic oscillator[J]. Optics and precision engineering, 2018, 26(2): 418-425.
DOI：

Yang LI, Zhi-bin Lin, Xiao-lin HUANG. Polar radius moment invariants criterion for identifying bearing early failure by chaotic oscillator[J]. Optics and precision engineering, 2018, 26(2): 418-425. DOI： 10.3788/OPE.20182602.0418.

摘要

现有的基于混沌振子检测轴承故障的方法的关键步骤是混沌振子相态转变判别，目前大多采用李雅普诺夫指数等特征值进行判断，针对其计算过程复杂，耗费时间长的缺点，基于图像识别技术，提出了一种以极半径不变矩参数作为相态转变的识别方法。通过构造Duffing混沌振子，分析了其相态转变与周期策动力的变化关系，证明其用于轴承早期故障识别的可行性；给出了极半径不变矩的定义，并证明在混沌振子相图由混沌运动态向大尺度周期态转变的过程中，随着周期摄动力不断增加，极半径不变矩表现出单调递增的特性；与HU氏不变矩及二维近似熵判别方法进行对比，讨论了极半径不变矩的抗噪声干扰能力；最终，将该方法用于实际搭建的钻机动力头轴承早期故障诊断的试验中。试验结果表明：极半径不变矩可以识别混沌振子相态过程转变，最低检测信噪比达到-36.99 dB，且识别准确率也较另外两种方法提高了4%~7%。证明该方法可以用于轴承早期故障识别，具有识别准确率高，抗噪声干扰能力强，计算简便的优点。

Abstract

The existing methods for detecting bearing faults based on chaotic oscillators have been successfully applied. The key step of the method is to distinguish the phase transition of chaotic oscillators. Lyapunov exponents are usually used to judge the transformation

which is complicated and time-consuming. Starting with image recognition

an identification method based on polar radius invariant moment parameter for phase transformation was proposed. The Duffing chaotic oscillator was constructed

and the relationship between the phase transition and the cyclic dynamic force was analyzed

and the feasibility of the early fault identification for the bearing by chaotic oscillators was proved. Then

the definition of polar radius invariant moment was given

and it was proved that the value of performance was monotonically increasing in the process of chaotic oscillator phase transition from chaos to large-scale periodic state with the periodic perturbation increasing. The anti noise capability of polar radius invariant moments was discussed and compared with the HU's invariant moments and the two-dimensional approximate entropy method. Finally

the method was applied to the test of the early fault diagnosis in the power head bearing of drilling machine. The experimental results show that the polar radius invariant moment could identify the phase transition of chaotic oscillator

and the minimum detection signal-to-noise ratio was -36.99 dB

and the accuracy of recognition was improved by 4%-7% comparing with the other two methods. The method proposed can be used in early identification of bearing faults and has the advantages of high recognition accuracy

strong noise immunity and simple calculation.

关键词

Keywords

references

赵吉文, 俞巧云, 王建平, 等.往复式机械非平稳信号的混沌与分形[J].光学精密工程, 2003, 11(6):637-642.

ZHAO J W, YU Q Y, WANG J P, et al.. Study of fault signals from main bearing of internal-combustion engine using chaos and fractal theories[J]. Opt. Precision Eng., 2003, 11(6):637-642. (in Chinese)

GALVANETTO U. Numerical computation of Lyapunov exponents in discontinuous maps implicitly defined[J]. Computer Physics Communications, 2000, 131(1-2):1-9.

石建飞, 张艳龙, 王丽, 等. Duffing系统在双参数平面上的动力学特性分析[J].应用力学学报, 2017, 34(2):250-256.

SHI J F, ZHANG Y L, WANG L, et al.. Dynamic character analysis of Duffing system in two-parameter plane[J]. Chinese Journal of Applied Mechanics, 2017, 34(2):250-256. (in Chinese)

李国正, 张波.基于Duffing振子检测频率未知微弱信号的新方法[J].仪器仪表学报, 2017, 38(1):181-189.

LI G ZH, ZHANG B. Novel method for detecting weak signal with unknown frequency based on Duffing oscillator[J]. Chinese Journal of Scientific Instrument, 2017, 38(1):181-189. (in Chinese)

LUO A C J, O'CONNOR D M. On periodic motions in a parametric hardening Duffing oscillator[J]. International Journal of Bifurcation and Chaos, 2014, 24(1):1430004.

JI CH P, GUO W P, JI H H, et al.. Unknown weak signal detection based on Duffing oscillator[J]. Sensors & Transducers, 2014, 162(1):190-196.

孙慧贤, 张玉华, 罗飞路.航空发动机篦齿盘表面裂纹的视觉检测[J].光学精密工程, 2009, 17(5):1187-1195.

SUN H X, ZHANG Y H, LUO F L. Visual inspection of surface crack on labyrinth disc in aeroengine[J]. Opt. Precision Eng., 2009, 17(5):1187-1195. (in Chinese)

郭树旭, 赵静, 李雪妍.基于中心-轮廓距离特征统计的形状表示方法[J].电子与信息学报, 2015, 37(6):1365-1371.

GUO SH X, ZHAO J, LI X Y. Research on shape representation based on statistical features of centroid-contour distance[J]. Journal of Electronics & Information Technology, 2015, 37(6):1365-1371. (in Chinese)

曹茂永, 孙农亮, 郁道银.用于模式识别的极半径不变矩[J].计算机学报, 2004, 27(6):860-864.

CAO M Y, SUN N L, YU D Y. Polar-radius-invariant-moment for pattern recognition[J]. Chinese Journal of Computers, 2004, 27(6):860-864. (in Chinese)

田秋红, 孙政荣.基于Hu不变矩和BP网络的条形码图像识别方法[J].计算机工程与设计, 2012, 33(4):1563-1568.

TIAN Q H, SUN ZH R. Bar code recognition method based on invariable moment and BP network classifier[J]. Computer Engineering and Design, 2012, 33(4):1563-1568. (in Chinese)

BOLDYŠ J, FLUSSER J. Extension of moment features' invariance to blur[J]. Journal of Mathematical Imaging and Vision, 2008, 32(3):227-238.

李强, 王太勇, 胥永刚, 等.基于混沌和二维近似熵的滚动轴承故障诊断[J].振动工程学报, 2007, 20(3):268-273.

LI Q, WANG T Y, XU Y G, et al.. Fault diagnosis of rolling bearings based on chaos and two-dimensional approximate entropy[J]. Journal of Vibration Engineering, 2007, 20(3):268-273. (in Chinese)

YAN R Q, GAO R X. Approximate entropy as a diagnostic tool for machine health monitoring[J]. Mechanical Systems and Signal Processing, 2007, 21(2):824-839.

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